# Difference between revisions of "Fairness Without Demographics in Repeated Loss Minimization"

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− | At this point our goal is to minimize the worst-case group risk over a single time-step <math display="inline">R </math> | + | At this point our goal is to minimize the worst-case group risk over a single time-step <math display="inline">\mathcal{R} </math> |

## Revision as of 14:44, 19 October 2018

This page contains the summary of the paper "Fairness Without Demographics in Repeated Loss Minimization" by Hashimoto, T. B., Srivastava, M., Namkoong, H., & Liang, P. which was published at the International Conference of Machine Learning (ICML) in 2018. In the following, an

## Contents

# Overview of the Paper

# Introduction

## Fairness

# Example and Problem Setup

# Why Empirical Risk Minimization (ERM) does not work

# Distributonally Robust Optimization (DRO)

## Risk Bounding Over Unknown Groups

At this point our goal is to minimize the worst-case group risk over a single time-step [math]\mathcal{R} [/math]